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Results 31 - 40 of 76 for conv_2d (0.32 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
// CHECK: %[[VAL_5:.*]] = "tfl.conv_2d"(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
rewriter.replaceOp(splitv_op, slice_outputs); return success(); } // ================== conv_2d ======================== LogicalResult EnsureBiasForConv2d::matchAndRewrite( TFL::Conv2DOp conv_op, PatternRewriter& rewriter) const { return EnsureBias(conv_op, 2, rewriter); } // ================== slice ============================
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
// CHECK: %[[VAL_12:.*]] = "tfl.reshape"(%[[VAL_10]], %[[VAL_7]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x512xf32>, tensor<4xi32>) -> tensor<128x1x1x512xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
// object containing configuration parameters, builtins have a predetermined // set of acceptable options. // LINT.IfChange enum BuiltinOperator : int32 { ADD = 0, AVERAGE_POOL_2D = 1, CONCATENATION = 2, CONV_2D = 3, DEPTHWISE_CONV_2D = 4, DEPTH_TO_SPACE = 5, DEQUANTIZE = 6, EMBEDDING_LOOKUP = 7, FLOOR = 8, FULLY_CONNECTED = 9, HASHTABLE_LOOKUP = 10, L2_NORMALIZATION = 11, L2_POOL_2D = 12,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
fused_activation_function:ActivationFunctionType; dilation_w_factor:int = 1; dilation_h_factor:int = 1; // Parameters for Conv2D version 8 or above. // When set, quantized_bias_type defines the dtype for both bias and accumulator. quantized_bias_type: TensorType; } // Options for both Conv3D and Conv3DTranspose. table Conv3DOptions { padding:Padding; stride_d:int; stride_w:int; stride_h:int;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// Only rank size four input will be only available by the tf.Conv2D // operator verification. if (!input_type || input_type.isDynamicDim(3)) { return failure(); } // Check if the given op is based on grouped convolution. // Dim size zero will be verified by the tf.Conv2D operator verification. if (input_type.getDimSize(3) % filter_type.getDimSize(2) != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
// with a 0-d constant, e.g. before this optimization, // %cst = arith.constant dense<1.0> : tensor<16x16x4xf32> // %0 = "tfl.conv_2d"... // %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<16x16x4xf32>) // After this optimization: // %cst = arith.constant dense<1.0> : tensor<f32> // %0 = "tfl.conv_2d"... // %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
const auto& subgraph = model_.subgraphs[0]; auto conv_op = subgraph->operators[0].get(); const int input_tensor_idx = 0; const int weights_tensor_idx = 1; const int bias_tensor_index = 2; const int output_tensor_idx = 0; const auto bias_tensor = subgraph->tensors[conv_op->inputs[bias_tensor_index]].get(); const auto input_tensor = subgraph->tensors[conv_op->inputs[input_tensor_idx]].get();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
%0 = "tf.Conv2D"(%arg0, %arg1) { data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32> %1 = "tf.Relu6"(%0) {device = ""} : (tensor<*xf32>) -> tensor<*xf32> %3 = "tf.Conv2D"(%arg0, %arg1) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
} else if (function_name.contains("conv2d")) { // For Conv2D, the channel dimension must be static to calculate the // feature group count. if (!HasStaticShapeAtDims(call_op->getOperand(0), /*dims=*/3)) { return absl::InternalError( "The channel dimension of Conv2D is required to be static."); } } else if (function_name.contains("conv3d")) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0)